COMPUTER SCIENCE COLLOQUIUM

Semantic Relatedness Applied to All Words Sense Disambiguation

JASON MICHELIZZI
Computer Science Graduate Student

Thursday, June 16, 2005
10:00 a.m.

HELLER HALL 306

ABSTRACT

Measures of semantic relatedness have many applications, including word
sense disambiguation.  This thesis presents an algorithm for all words
sense disambiguation that incorporates measures of semantic relatedness.

A variety of measures of semantic relatedness and semantic similarity have
been proposed.  These measures are founded on a number of different
principles.  There are measures that rely on path length in a taxonomy,
information content, finding lexical chains, and comparing the similarity
of dictionary glosses.  The strengths and weaknesses of the measures are
discussed in detail, and a method of handling multiple inheritances in a
taxonomy is developed.

A method of word sense disambiguation that employs measures of semantic
relatedness for an all words task is defined.  This system will assign a
sense to every content word in the text that is found in our dictionary
(WordNet).  This algorithm is unique because it does not require training
and is capable of disambiguating arbitrary text.

The algorithm is evaluated using an assortment of sense-tagged corpora,
including SemCor and the test data for Senseval-2 and Senseval-3.  The
experimental results are compared to the performance of other existing all
words disambiguation systems.  The results are very promising, and the
system presented in this thesis is highly competitive with other systems.






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